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2021

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Articles 421 - 439 of 439

Full-Text Articles in Databases and Information Systems

Analyzing Tweets On New Norm: Work From Home During Covid-19 Outbreak, Swapna Gottipati, Kyong Jin Shim, Hui Hian Teo, Karthik Nityanand, Shreyansh Shivam Jan 2021

Analyzing Tweets On New Norm: Work From Home During Covid-19 Outbreak, Swapna Gottipati, Kyong Jin Shim, Hui Hian Teo, Karthik Nityanand, Shreyansh Shivam

Research Collection School Of Computing and Information Systems

The COVID-19 pandemic triggered a large-scale work-from-home trend globally in recent months. In this paper, we study the phenomenon of “work-from-home” (WFH) by performing social listening. We propose an analytics pipeline designed to crawl social media data and perform text mining analyzes on textual data from tweets scrapped based on hashtags related to WFH in COVID-19 situation. We apply text mining and NLP techniques to analyze the tweets for extracting the WFH themes and sentiments (positive and negative). Our Twitter theme analysis adds further value by summarizing the common key topics, allowing employers to gain more insights on areas of …

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Deep Unsupervised Anomaly Detection, Tangqing Li, Zheng Wang, Siying Liu, Wen-Yan Lin Jan 2021

Deep Unsupervised Anomaly Detection, Tangqing Li, Zheng Wang, Siying Liu, Wen-Yan Lin

Research Collection School Of Computing and Information Systems

This paper proposes a novel method to detect anomalies in large datasets under a fully unsupervised setting. The key idea behind our algorithm is to learn the representation underlying normal data. To this end, we leverage the latest clustering technique suitable for handling high dimensional data. This hypothesis provides a reliable starting point for normal data selection. We train an autoencoder from the normal data subset, and iterate between hypothesizing normal candidate subset based on clustering and representation learning. The reconstruction error from the learned autoencoder serves as a scoring function to assess the normality of the data. Experimental results …

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The Value Of Humanization In Customer Service, Yang Gao, Huaxia Rui, Shujing Sun Jan 2021

The Value Of Humanization In Customer Service, Yang Gao, Huaxia Rui, Shujing Sun

Research Collection School Of Computing and Information Systems

As algorithm-based agents become increasingly capable of handling customer service queries, customers are often uncertain whether they are served by humans or algorithms, and managers are left to question the value of human agents once the technology matures. The current paper studies this question by quantifying the impact of customers' enhanced perception of being served by human agents on customer service interactions. Our identification strategy hinges on the abrupt implementation by Southwest Airlines of a signature policy, which requires the inclusion of an agent's first name in responses on Twitter, thereby making the agent more humanized in the eyes of …

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Chronic Customers Or Increased Awareness? The Dynamics Of Social Media Customer Service, Shujing Sun, Yang Gao, Huaxia Rui Jan 2021

Chronic Customers Or Increased Awareness? The Dynamics Of Social Media Customer Service, Shujing Sun, Yang Gao, Huaxia Rui

Research Collection School Of Computing and Information Systems

Despite that social media has become a promising alternative to traditional call centers, managers hesitate to fully harness its power because they worry that active service intervention may encourage excessive use of the channel by disgruntled customers. This paper sheds light on such a concern by examining the dynamics between brand-level customer complaints and service interventions on social media. Using details of customer-brand interactions of 40 airlines on Twitter, we find that more service interventions indeed cause more customer complaints, accounting for the online customer population and service quality. However, the increased complaints are primarily driven by the awareness enhancement …

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Smart Contracts: Will Fintech Be The Catalyst For The Next Global Financial Crisis?, Randall Duran, Paul Griffin Jan 2021

Smart Contracts: Will Fintech Be The Catalyst For The Next Global Financial Crisis?, Randall Duran, Paul Griffin

Research Collection School Of Computing and Information Systems

Purpose: This paper aims to examine the risks associated with smart contracts, a disruptive financial technology (FinTech) innovation, and assesses how in the future they could threaten the integrity of the global financial system. Design/methodology/approach: A qualitative approach is used to identify risk factors related to the use of new financial innovations, by examining how over-the-counter (OTC) derivatives contributed to the Global Financial Crisis (GFC) which occurred during 2007 and 2008. Based on this analysis, the potential for similar concerns with smart contracts are evaluated, drawing on the failure of The DAO on the Ethereum blockchain, which involved the loss …

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Coherence And Identity Learning For Arbitrary-Length Face Video Generation, Shuquan Ye, Chu Han, Jiaying Lin, Guoqiang Han, Shengfeng He Jan 2021

Coherence And Identity Learning For Arbitrary-Length Face Video Generation, Shuquan Ye, Chu Han, Jiaying Lin, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

Face synthesis is an interesting yet challenging task in computer vision. It is even much harder to generate a portrait video than a single image. In this paper, we propose a novel video generation framework for synthesizing arbitrary-length face videos without any face exemplar or landmark. To overcome the synthesis ambiguity of face video, we propose a divide-and-conquer strategy to separately address the video face synthesis problem from two aspects, face identity synthesis and rearrangement. To this end, we design a cascaded network which contains three components, Identity-aware GAN (IA-GAN), Face Coherence Network, and Interpolation Network. IA-GAN is proposed to …

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Proxy-Free Privacy-Preserving Task Matching With Efficient Revocation In Crowdsourcing, Jiangang Shu, Kan Yang, Xiaohua Jia, Ximeng Liu, Cong Wang, Robert H. Deng Jan 2021

Proxy-Free Privacy-Preserving Task Matching With Efficient Revocation In Crowdsourcing, Jiangang Shu, Kan Yang, Xiaohua Jia, Ximeng Liu, Cong Wang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Task matching in crowdsourcing has been extensively explored with the increasing popularity of crowdsourcing. However, privacy of tasks and workers is usually ignored in most of exiting solutions. In this paper, we study the problem of privacy-preserving task matching for crowdsourcing with multiple requesters and multiple workers. Instead of utilizing proxy re-encryption, we propose a proxy-free task matching scheme for multi-requester/multi-worker crowdsourcing, which achieves task-worker matching over encrypted data with scalability and non-interaction. We further design two different mechanisms for worker revocation including ServerLocal Revocation (SLR) and Global Revocation (GR), which realize efficient worker revocation with minimal overhead on the …

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Context-Aware Retrieval-Based Deep Commit Message Generation, Haoye Wang, Xin Xia, David Lo, Qiang He, Xinyu Wang, John Grundy Jan 2021

Context-Aware Retrieval-Based Deep Commit Message Generation, Haoye Wang, Xin Xia, David Lo, Qiang He, Xinyu Wang, John Grundy

Research Collection School Of Computing and Information Systems

Commit messages recorded in version control systems contain valuable information for software development, maintenance, and comprehension. Unfortunately, developers often commit code with empty or poor quality commit messages. To address this issue, several studies have proposed approaches to generate commit messages from commit diffs. Recent studies make use of neural machine translation algorithms to try and translate git diffs into commit messages and have achieved some promising results. However, these learning-based methods tend to generate high-frequency words but ignore low-frequency ones. In addition, they suffer from exposure bias issues, which leads to a gap between training phase and testing phase. …

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Creators And Backers In Rewards-Based Crowdfunding: Will Incentive Misalignment Affect Kickstarter's Sustainability?, Michael Wessel, Rob Gleasure, Robert John Kauffman Jan 2021

Creators And Backers In Rewards-Based Crowdfunding: Will Incentive Misalignment Affect Kickstarter's Sustainability?, Michael Wessel, Rob Gleasure, Robert John Kauffman

Research Collection School Of Computing and Information Systems

Incentive misalignment in rewards-based crowd-funding occurs because creators may benefit disproportionately from fundraising, while backers may benefit disproportionately from the quality of project deliverables. The resulting principal-agent relationship means backers rely on campaign information to identify signs of moral hazard, adverse selection, and risk attitude asymmetry. We analyze campaign information related to fundraising, and compare how different information affects eventual backer satisfaction, based on an extensive dataset from Kickstarter. The data analysis uses a multi-model comparison to reveal similarities and contrasts in the estimated drivers of dependent variables that capture different outcomes in Kickstarter’s funding campaigns, using a linear probability …

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Sustainability Of Rewards-Based Crowdfunding: A Quasi-Experimental Analysis Of Funding Targets And Backer Satisfaction, Michael Wessel, Rob Gleasure, Robert John Kauffman Jan 2021

Sustainability Of Rewards-Based Crowdfunding: A Quasi-Experimental Analysis Of Funding Targets And Backer Satisfaction, Michael Wessel, Rob Gleasure, Robert John Kauffman

Research Collection School Of Computing and Information Systems

Rewards-based crowdfunding presents an information asymmetry for participants due to the funding mechanism used. Campaign-backers trust creators to complete projects and deliver rewards as outlined prior to the fundraising process, but creators may discover better opportunities as they progress with a project. Despite this, the all-or-nothing (AON) mechanism on crowdfunding platforms incentivizes creators to set meager funding-targets that are easier to achieve but may offer limited slack when creators wish to simultaneously pursue emerging opportunities later in the project. We explore the related issues of how funding targets seem to be selected by the creators, and how dissatisfaction with the …

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Do Blockchain And Iot Architecture Create Informedness To Support Provenance Tracking In The Product Lifecycle?, Somnath Mazumdar, Thomas Jensen, Raghava Rao Mukkamala, Robert John Kauffman, Jan Damsgaard Jan 2021

Do Blockchain And Iot Architecture Create Informedness To Support Provenance Tracking In The Product Lifecycle?, Somnath Mazumdar, Thomas Jensen, Raghava Rao Mukkamala, Robert John Kauffman, Jan Damsgaard

Research Collection School Of Computing and Information Systems

Consumers often lack information about the origin and provenance of the products they buy. They may ask: Is a food product truly organic? Or, what is the origin of the gemstone in the ring I purchased? They also may have sustainability concerns about the footprint of a product at the end of its life. Producers and sellers, meanwhile, wish to know how longitudinal tracking of the provenance of products and their components can boost their sales prices and after-market value, and re- veal new business opportunities. We focus on how the product lifecycle (PLC) can be leveraged to track information …

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Attribute-Aware Pedestrian Detection In A Crowd, Jialiang Zhang, Lixiang Lin, Jianke Zhu, Yang Li, Yun-Chen Chen, Yao Hu, Steven C. H. Hoi Jan 2021

Attribute-Aware Pedestrian Detection In A Crowd, Jialiang Zhang, Lixiang Lin, Jianke Zhu, Yang Li, Yun-Chen Chen, Yao Hu, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Pedestrian detection is an initial step to perform outdoor scene analysis, which plays an essential role in many real-world applications. Although having enjoyed the merits of deep learning frameworks from the generic object detectors, pedestrian detection is still a very challenging task due to heavy occlusions, and highly crowded group. Generally, the conventional detectors are unable to differentiate individuals from each other effectively under such a dense environment. To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion. Besides the typical semantic features, center position, target's scale, …

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A Continual Deepfake Detection Benchmark: Dataset, Methods, And Essentials, Chuqiao Li, Zhiwu Huang, Danda Pani Paudel, Yabin Wang, Mohamad Shahbazi, Xiaopeng Hong, Van Gool Luc Jan 2021

A Continual Deepfake Detection Benchmark: Dataset, Methods, And Essentials, Chuqiao Li, Zhiwu Huang, Danda Pani Paudel, Yabin Wang, Mohamad Shahbazi, Xiaopeng Hong, Van Gool Luc

Research Collection School Of Computing and Information Systems

There have been emerging a number of benchmarks and techniques for the detection of deepfakes. However, very few works study the detection of incrementally appearing deepfakes in the real-world scenarios. To simulate the wild scenes, this paper suggests a continual deepfake detection benchmark (CDDB) over a new collection of deepfakes from both known and unknown generative models. The suggested CDDB designs multiple evaluations on the detection over easy, hard, and long sequence of deepfake tasks, with a set of appropriate measures. In addition, we exploit multiple approaches to adapt multiclass incremental learning methods, commonly used in the continual visual recognition, …

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Activity Based Traffic Indicator System For Monitoring The Covid-19 Pandemic, Justin Junsay, Aaron Joaquin Lebumfacil, Ivan George Tarun, William Emmanuel S. Yu Jan 2021

Activity Based Traffic Indicator System For Monitoring The Covid-19 Pandemic, Justin Junsay, Aaron Joaquin Lebumfacil, Ivan George Tarun, William Emmanuel S. Yu

Department of Information Systems & Computer Science Faculty Publications

This study describes an activity based traffic indicator system to provide information for COVID-19 pandemic management. The activity based traffic indicator system does this by utilizing a social probability model based on the birthday paradox to determine the exposure risk, the probability of meeting someone infected (PoMSI). COVID-19 data, particularly the 7-day moving average of the daily growth rate of cases (7-DMA of DGR) and cumulative confirmed cases of next week covering a period from April to September 2020, were then used to test PoMSI using Pearson correlation to verify whether it can be used as a factor for the …

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Cura Personalis: Institutionalizing Compassion During Emergency Remote Teaching, Ma. Monica L. Moreno, Ma. Mercedes T. Rodrigo, Johanna Marion R. Torres, Timothy Jireh Gaspar, Jenilyn L. Agapito Jan 2021

Cura Personalis: Institutionalizing Compassion During Emergency Remote Teaching, Ma. Monica L. Moreno, Ma. Mercedes T. Rodrigo, Johanna Marion R. Torres, Timothy Jireh Gaspar, Jenilyn L. Agapito

Department of Information Systems & Computer Science Faculty Publications

Faced with the fears and anxieties brought on by the COVID-19 crisis, educational institutions had to devise new compassion-based teaching and learning policies and approaches that recognized and provided for the pandemic’s psychological and emotional toll. This paper describes how the Ateneo de Manila University in the Philippines enacted its core value of cura personalis, care for the entire person, in the context of emergency remote teaching. We describe the circumstances that prompted the greater emphasis on compassion and the adjustments to classroom management, course content, class interactions, and assessment. Finally we describe the tradeoffs or costs of this …

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Transactional Distances During Emergency Remote Teaching Experiences, Ma. Monica L. Moreno, Ma. Mercedes T. Rodrigo, Johanna Marion R. Torres, Timothy Jireh Gaspar, Jenilyn L. Agapito Jan 2021

Transactional Distances During Emergency Remote Teaching Experiences, Ma. Monica L. Moreno, Ma. Mercedes T. Rodrigo, Johanna Marion R. Torres, Timothy Jireh Gaspar, Jenilyn L. Agapito

Department of Information Systems & Computer Science Faculty Publications

The Transactional Distance Theory posits that successful remote learning occurs when teachers decrease psychological or transactional gaps. Narrowing the transactional distance can be achieved through a balance of appropriate course structure and dialogue, fostering healthy student autonomy in the process. This paper describes the Emergency Remote Teaching experiences of faculty and students of the Ateneo de Manila University in the Philippines. It examines these experiences in the context of the transactional distance framework. Findings show that a sudden shift to remote learning mandates greater student autonomy, which increases transactional distance. Because of this, efforts by faculty to increase student-teacher dialogue …

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Xiphias: Using A Multidimensional Approach Towards Creating Meaningful Gamification-Based Badge Mechanics, Jonathan D.L Casano, Jenilyn L. Agapito, Nicole Ann F. Tolosa Jan 2021

Xiphias: Using A Multidimensional Approach Towards Creating Meaningful Gamification-Based Badge Mechanics, Jonathan D.L Casano, Jenilyn L. Agapito, Nicole Ann F. Tolosa

Department of Information Systems & Computer Science Faculty Publications

This paper shows the design and initial testing of three new Xiphias Badges --Presence; Mastery; and Antifragility – based on the merging of the salient features from James Clear’s Behavior Change model (2016); Johann Hari’s Lost Connections model (2018); and Jordan Peterson’s recent interpretation of the Big Five model of Personality Traits (2007). This multidimensional approach is an attempt to cater to the multidimensionality of a user and aims to be a more universal gamification approach that taps into internal motivations. The badge mechanics were tested on 69 undergraduate students using a Low-Fidelity Gamified Tracker. The results of a survey …

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Comparison Of English Comprehension Among Students From Different Backgrounds Using A Narrative-Centered Digital Game, May Marie P. Talandron-Felipe, Kent Levi A. Bonifacio, Gladys S. Ayunar, Ma. Mercedes T. Rodrigo Jan 2021

Comparison Of English Comprehension Among Students From Different Backgrounds Using A Narrative-Centered Digital Game, May Marie P. Talandron-Felipe, Kent Levi A. Bonifacio, Gladys S. Ayunar, Ma. Mercedes T. Rodrigo

Department of Information Systems & Computer Science Faculty Publications

This paper reports the continuation of the field testing of a narrative-centered digital game for English comprehension called Learning Likha: Rangers to the Rescue (LLRR) with a two-fold goal: first, identify the differences in terms of usage, attitudes towards, and perceptions of the English language between students from southern Philippines and the National Capital Region, and second, to determine how the LLRR in-game performance, post-test comprehension scores, engagement, and motivation of students differ between the groups. The participants who are grade school students from a province in southern Philippines answered questionnaires about their attitude towards and perception of English, played …

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For People And Planet: Teachers’ Evaluation Of An Educational Mobile Game And Resource Pack, Ma. Mercedes T. Rodrigo, Johanna Marion R. Torres, Janina Carla M. Castro, Abigail Marie T. Favis, Ingrid Yvonne Herras, Francesco U. Amante, Hakeem Jimenez, Juan Carlo F. Mallari, Kevin Arnel C. Mora, Walfrido David A. Diy, Jaclyn Ting Ting M. Lim, Ma. Assunta C. Cuyegkeng Jan 2021

For People And Planet: Teachers’ Evaluation Of An Educational Mobile Game And Resource Pack, Ma. Mercedes T. Rodrigo, Johanna Marion R. Torres, Janina Carla M. Castro, Abigail Marie T. Favis, Ingrid Yvonne Herras, Francesco U. Amante, Hakeem Jimenez, Juan Carlo F. Mallari, Kevin Arnel C. Mora, Walfrido David A. Diy, Jaclyn Ting Ting M. Lim, Ma. Assunta C. Cuyegkeng

Department of Information Systems & Computer Science Faculty Publications

For People and Planet: An SDG Adventure refers to a freely available Android-based narrative adventure game and teacher resource pack that helps learners see the United Nations Sustainable Development Goals (SDGs) in their day-to-day lives. In this paper, we describe the results of an evaluation of both the game and the resource pack by eight (8) middle school teachers. After playing the game and reading the resource pack, teachers gave their feedback about what they liked best and least about the materials, how they could use these resources for their classes, and how these resources could be improved further. Overall, …

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